DiffusionE

Official code for the paper "DiffusionE: Reasoning on Knowledge Graphs via Diffusion-based Graph Neural Networks"

Dependencies

• torch == 1.12.1
• torch_scatter == 2.0.9
• numpy == 1.21.6
• scipy == 1.10.1

Reproduction

Transductive settings (in \transductive)

Reproduction with training scripts

Family dataset

python3 train.py --data_path ./data/family/ --train --topk 100 --layers 8 --fact_ratio 0.90 --gpu 0

UMLS dataset

python3 train.py --data_path ./data/umls/ --train --topk 100 --layers 5 --fact_ratio 0.90 --gpu 0

WN18RR dataset

python3 train.py --data_path ./data/WN18RR/ --train --topk 1000 --layers 8 --fact_ratio 0.96 --gpu 0

FB15k-237 dataset

python3 train.py --data_path ./data/fb15k-237/ --train --topk 2000 --layers 7 --fact_ratio 0.99 --remove_1hop_edges --gpu 0

NELL995 dataset

python3 train.py --data_path ./data/nell/ --train --topk 2000 --layers 6 --fact_ratio 0.95 --gpu 0

YAGO3-10 dataset

python3 train.py --data_path ./data/YAGO/ --train --topk 1000 --layers 8 --fact_ratio 0.995 --gpu 0

Inductive settings (in \inductive)

Reproduction with training scripts

The full training scripts can be found in inductive/reproduce.sh.

For example, training on WN18RR v1 dataset:

python3 train.py --data_path ./data/WN18RR_v2 --gpu 1
python3 train.py --data_path ./data/fb237_v1 --gpu 1
python3 train.py --data_path ./data/nell_v1 --gpu 4